PS50: Statistical Considerations on Non-Inferiority Study Design

Non-inferiority (NI) trials represent a critical component of modern drug development when establishing that a new treatment is effective by demonstrating that it is not unacceptably worse than an established active control, usually because a superiority study design (drug versus placebo, dose response, or superiority to an active drug) cannot be used. This comprehensive session will examine the statistical foundations, regulatory considerations, and practical challenges in designing robust non-inferiority studies, covering fundamental principles including margin selection and justification using fixed margin and synthesis approaches that integrate historical data with clinical input. The session will explore the unique hypothesis testing framework in NI trials, contrasting with superiority and equivalence paradigms, and address contemporary analytical considerations including estimand frameworks and related methodological challenges specific to non-inferiority studies. Key design considerations include multiplicity handling strategies for studies with co-primary endpoints, noting that separate non-inferiority margin justifications are required for each endpoint. Contemporary challenges will be explored, including missing data management strategies specific to NI trials where missingness can bias results toward non-inferiority conclusions, assay sensitivity requirements to demonstrate reliable detection of treatment differences, and treatment switching scenarios within estimand frameworks. The session will feature current FDA regulatory perspectives and practical insights, supplemented by case studies across different therapeutic areas. Emerging methodologies will be explored, including Bayesian approaches and real-world evidence integration in NI trial optimization, providing participants with applicable knowledge for regulatory biostatistics practice and clinical trial design in the evolving landscape of drug development.